Technical debt reduction with AI agents

Q: Will this slow down feature delivery?

No – paydown runs in parallel, module by module, with your team spending review time, not implementation time.

Q: How do you measure "reduced" technical debt?

Through the debt map's scoring – dependencies, duplication, complexity, and coverage gaps — re-scored after each paydown phase.

Q: What kind of technical debt can agents actually handle?

Outdated dependencies, dead code, duplicated logic, missing tests, and undocumented modules are strong fits. Deep architectural decisions still need human-led design work.

Agent-scanned codebase, human-approved fixes, one team accountable for every merge – from first assessment to debt that stays paid down.

Who has benefited from Boldare's expertise?

See the companies that trusted Boldare to get it done.

Everything you need for technical debt reduction with AI agents

Most technical debt doesn't shrink because nobody has time – it shrinks when assessment, cleanup, refactoring, and monitoring get handled by people and agents who specialize in each part. Here's how we cover the full scope, end to end.

Debt assessment

Technical debt assessment: a scored, prioritized map of your codebase – built before a single line of code changes.

• Full codebase scan Agents inventory dependencies, dead code, duplication, and complexity hotspots across the entire repo. Manual audits typically sample 10–20% of a codebase; agent-based scans cover 100%.
• Risk-scored findings Every issue ranked by breakage risk and fix effort, turning "this needs cleanup" into a number leadership can act on.
• Prioritized roadmap A sequenced paydown plan with time estimates per module – the structure that separates debt reduction that ships from debt reduction that stays a slide deck.
• Dependency mapping Outdated packages, security vulnerabilities, and version drift surfaced automatically – the layer of debt most audits miss because it's invisible until something breaks.
• Go/no-go recommendation A concrete call on incremental paydown vs. targeted rewrite, backed by assessment data, not intuition.
• Since 2004 Product development experience that predates "technical debt" as an industry term, applied to how every assessment is structured today.

Dependency cleanup

Dependency and dead code cleanup: the unglamorous debt that compounds fastest, cleared systematically instead of manually.

• Automated dead code removal Agents flag and remove unused modules and functions at a scale manual review can't match.
• Dependency upgrades Deprecated and vulnerable packages updated in sequence, with compatibility verified before merge.
• Duplication elimination Repeated logic consolidated across the codebase instead of patched file by file.
• Version drift correction Framework and library versions brought back in line before drift compounds into a forced migration.
• Specialized delivery teams Cleanup handled by engineers who work this problem repeatedly, not generalists splitting attention across the backlog.
• Production-safe pacing Cleanup runs module by module; nothing ships without review and approval first.

Agent-led refactoring

AI-assisted refactoring: our agents propose the change, our engineers review and approve it – every merge gated by a senior developer, not an unsupervised agent.

• Proven refactoring patterns Transformations reused across engagements, so agents aren't improvising structure on your production code.
• Incremental migration Feature-flag-based cutover keeps production live throughout, instead of freezing the roadmap for a big-bang rewrite.
• Framework-aware transforms Refactors respect target-framework idioms (PHP, Java, .NET, Node.js) rather than applying a generic pattern everywhere.
• Human review gates on every change No agent-proposed refactor merges without your team's sign-off.
• Architecture-level flags Beyond line-by-line cleanup, agents surface structural debt that needs a human design decision, instead of silently working around it.
• Claude Code-native workflow Refactoring runs inside the same AI-native tooling your engineering team may already be evaluating.

Test coverage & safety

Test coverage and safety nets: debt reduction that doesn't introduce regressions nobody notices until production.

• Coverage backfill Agents generate tests for previously undocumented or untested modules before refactoring touches them.
• Before/after comparison Test coverage reports flag regressions before merge, not after deploy.
• Regression detection Automated comparison against baseline behavior, so "it still works" is verified, not assumed.
• Staged rollout safety Changes ship behind feature flags, so a bad refactor rolls back without becoming a production incident.
• QA-in-the-loop Testing runs inside the same pipeline as the refactor, not as a separate phase nobody schedules.
• Zero feature freeze Coverage work runs in parallel with your existing roadmap, not instead of it.

Documentation

AI-generated documentation: the debt that's invisible until the one engineer who understood it leaves.

• Auto-generated docs Every module touched during paydown gets documentation as part of the process, not as a follow-up task.
• Undocumented legacy coverage Agents document existing code that was never written down, closing the knowledge gap retroactively.
• Living documentation Docs update automatically as code changes, instead of going stale within weeks.
• Dependency context Documentation captures why a dependency exists – the context that usually lives only in someone's head.
• Onboarding-ready output New engineers ramp on a module from generated docs, without the original author in the room.
• Zero manual authoring effort Documentation is a byproduct of the paydown work, not a separate line item to budget.

Continous monitoring

Continuous debt monitoring: because technical debt is ongoing, not a one-time project with a finish line.

• Background agent monitoring Agents run continuously post-paydown, flagging new debt as it forms instead of waiting for the next audit.
• Dependency alerts Automated notifications for newly deprecated packages, vulnerabilities, and version drift as they appear.
• Complexity creep detection New complexity flagged as it's introduced, before it compounds into the next expensive rewrite.
• Quarterly architecture review A human-led review of system direction.
• Tiered retainer options Single-agent monitoring or a full agent pipeline, sized to how fast your codebase actually changes.
• Debt that doesn't silently return Paydown gains protected instead of eroding quietly over the following year and a half.

Got debt slowing your roadmap down?

Describe what's in your codebase, where the debt is hurting most, and how much risk you can take on. We'll come back with a concrete recommendation.

Head of DeliveryAleksandra Krzyżakbeata.sumera@boldare.com

Technical debt reduction across PHP, Symfony, Java, .NET, and beyond

Every codebase carries debt differently – these are where we go deepest, but the agent-led workflow transfers to any stack we assess.

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Here's what clients say about working with us.

Norbert Baumann

VP R&D – Sonnen

"They treat the customer portal as their product and this resulted in the high quality of their work."

Head of Software Development

Prisma

"They had a very short ramp-up time and were dedicated to delivering."

Zvonko Grujic

Director Digital Engineering – Maxeon Solar Technologies

"I feel that my opinions and observations matter and that the team will adjust their actions based on our feedback."

Jerome Defillon

Chief Technology Officer – Novolyze

"We were impressed with their capacity to embrace an unknown domain and challenge the strong assumptions presented."

Fabio Zecchini

Chief Technology Officer – Musement TUI Group

Boldare delivers results that meet our standards and expectations."

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Head of Engineering – BlaBlaCar

"Their customer-focused, Agile approach inspired us, and we discovered that we shared a similar mindset."

Allan Wilson

President - Team Alert

"I was really impressed with how much they cared about our product."

From assessment to production: guides for teams reducing technical debt with AI agents

The engineering decisions behind AI-assisted debt reduction that actually holds up in production – governance, review gates, and what breaks when teams skip them.

BLOGPOST

AI agents promise continuous debt paydown without pulling engineers off the roadmap. Poland is increasingly where this work happens well – driven by skill, not cost. 81% of executives say debt already constrains their AI initiatives; only 23% have scaled agentic AI past experimentation.

BLOGPOST

Every product carries some technical debt – those decisions made two years ago under time or budget pressure that never got revisited. The real questions aren't whether debt exists, but how to measure it and how much is actually healthy.

One partner from idea to launch

430+ client relationships. One full-stack team

You own it from day one

Documented, structured, and handed off so your team can maintain and extend it without us. We build for independence, not dependency.

Design and code in one team

We eliminate the handoff gap. Designers and developers work in parallel, not in sequence. What you see in the design file is what ships in production.

AI-native by default

Built to work with your AI tools. We structure and document what we build so tools like Cursor, Claude Code, and Figma Make generate output that stays within your standards – not outside them.

Built for your stack

We work with the tools and frameworks your team already uses – or we adapt to them. What we build fits your team, not the other way around.

Fixed price. No surprises

Every engagement starts with a clear scope and a fixed price. You know what you're getting before anything begins.

One source of truth. For everyone

Designers, developers, and AI tools all work from the same source. No parallel versions. No guessing which one is current.

Governance included

Nothing we deliver drifts without ownership. Every build includes a clear model for who decides on changes, how they're requested, and how things evolve as your product grows.

430+ client relationships

Every product we've built required decisions about what to standardise and what to keep flexible. That experience shapes everything we deliver.

Technical debt reduction using AI agents: what actually works in production

A practical guide to reducing technical debt with AI agents – covering agentic AI implementation, legacy code modernization, and the governance decisions that determine whether debt reduction holds up or has to be redone.

How is agent-led debt reduction different from a linter or static analysis tool?

Static analysis flags issues; it doesn't fix them. AI agents propose and implement the actual refactor – your team reviews the diff, not just the warning.

Will this slow down feature delivery?

How do you measure "reduced" technical debt?

Can AI agents create new technical debt instead of reducing it?

Yes, if agent output ships without review – this is the most common failure mode in 2026. Every agent-proposed change here goes through a human approval gate before merge specifically to prevent it.

What kind of technical debt can agents actually handle?

Do AI agents need architectural context to avoid creating debt?

Yes. Agents operating without documented conventions – naming, error handling, architectural boundaries – tend to generate code that compiles but violates decisions that become expensive to undo later. We codify this context (in a CLAUDE.md-style file) so agents work within your architecture instead of around it.

Which changes should never be approved by AI alone, even with review?

Core domain logic, security-critical components, and anything touching inter-service contracts require senior engineer sign-off regardless of AI involvement – these categories are defined explicitly before any agent-assisted work starts, not decided case by case.

What happens to code or data agents shouldn't see during debt reduction?

Credentials, PII, and security-critical modules are excluded from agent access before work begins – this governance is defined upfront, not handled reactively after something's already been exposed.

Does reducing technical debt with AI agents replace the need for senior engineers?

No – it reallocates their time. Senior engineers set the architectural standards agents operate within and review the changes where judgment matters (domain logic, security, cross-service impact); they spend less time on scaffolding and routine refactoring, not less time on decisions.

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